Experimental Design and Data Analysis – Bali 2010
Title: Experimental Design & Data Analysis – Bali 2010
Instructions for participants: here
Registration: Registration is Closed
Type: Workshop
Venue: Bali Botanic Garden
Dates: 12-16 July 2010 (5 days)
Organiser: Association for Tropical Biology & Conservation Asia-Pacific chapter and University of Indonesia
Sponsors: Asia-Pacific Network for Global Change and Xishuangbanna Tropical Botanic Garden
Fellowships: Travel awards and fellowships to cover the course fees are available for participants from lower-income countries in the Asia-Pacific region. Preference will be given to participants who are presenting a paper (oral or poster) at the Association for Tropical Biology & Conservation meeting (19-23 Jul) in Bali. A limited number of fellowships to cover the conference fees are also available.
Fees: $150 (including transport from airport, accommodation and food)
About: The ATBC Asia-Pacific chapter is running two courses at the above workshop, which is being held in conjunction with the ATBC Bali 2010 meeting. The Introductory course is targeted at post-graduate level participants who have received only very basic training in statistics. It will introduce basic concepts in experimental design and sampling and how to analyse data using traditional modeling methods, including ANOVA and regression. The Advanced course assumes participants are familiar with these basic methods and will introduce maximum likelihood approaches, more advance GLMs and multivariate methods. Please see the course outlines given below. Practicals for both courses will be conducted in R – a free opensource statistical computing program – and will provide an introduction to its use. Participants are required to bring their own laptop computers.
Please see the detailed schedule of topics for both courses below.
| Introductory course | ||
| Topic | ||
| DAY 1 | Lesson 1 | Scientific methodology |
| Lesson 2 | Regression and ANOVA | |
| Practical 1 | Regression and ANOVA by hand | |
| Lesson 3 | Multi-factor models | |
| Practical 2 | Regression and ANOVA in R | |
| DAY 2 | Lesson 4 | Assumptions of parametric models |
| Lesson 5 | Transformations | |
| Lesson 6 | Non-parametric rank tests | |
| Practical 3 | Non-parametric rank tests | |
| Practical 4 | Graphs in R | |
| DAY 3 | Lesson 7, 8 | General linear models and dummy variables |
| Lesson 9 | Model fit and simplification | |
| Practical 5 | Multi-factor models | |
| Lesson 10 | Sampling | |
| Practical 6 | Sampling | |
| DAY 4 | Lesson 11 | Experimental design |
| Lesson 12 | Single factor designs | |
| Lesson 13 | Nested designs and variance component analysis | |
| Practical 7 | Single factor designs | |
| DAY 5 | Lesson 14 | Multi-factor designs |
| Practical 8 | Multi-factor models | |
| Lesson 15 | Introduction to generalised linear models | |
| Lesson 16 | Count data | |
| Lesson 17 | Binary and proportion data | |
| Practical 9 | Generalised linear models | |
| Advanced course | ||
| Topic | ||
| DAY 1 | Lesson 1 | Framework for ecological modeling: Classical frequentist vs. likelihood approaches |
| Practical 1 | Introduction to R Data import, Data frames and matrices, Checking data |
|
| DAY 2 | Lesson 2 | Maximum likelihood estimation |
| Practical 2 | Exploratory data analysis with R | |
| Practical 3 | Introduction to R II Classical anova and regression, checks for violations and interpreting results with continuous and categorial variables |
|
| DAY 3 | Lesson 3 | Introduction to GLM, GLM for count data |
| Practical 4 | GLM for count data | |
| Lesson 4 | GLM for binary and proportion data | |
| Practical 5 | GLM for binary and proportion data | |
| DAY 4 | Lesson 5 | Model selection procedures |
| Lesson 6 | Mixed effects models | |
| Practical 6 | Mixed effects models | |
| DAY 5 | Lesson 7 | Introduction to multivariate analysis |
| Practical 7 | Ordination methods | |
| Lesson 8 | Methods for community datasets | |
| Practical 8 | Variation partitioning | |
